Overview

Dataset statistics

Number of variables30
Number of observations564211
Missing cells0
Missing cells (%)0.0%
Duplicate rows36878
Duplicate rows (%)6.5%
Total size in memory129.1 MiB
Average record size in memory240.0 B

Variable types

Numeric20
Categorical10

Alerts

Dataset has 36878 (6.5%) duplicate rowsDuplicates
first_browser has a high cardinality: 52 distinct values High cardinality
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_bookingHigh correlation
days_from_account_created_until_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with weekofyear_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
dayofweek_first_active is highly correlated with dayofweek_first_booking and 1 other fieldsHigh correlation
weekofyear_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_first_booking is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_booking is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_first_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_account_created is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_bookingHigh correlation
days_from_account_created_until_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with weekofyear_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
dayofweek_first_active is highly correlated with dayofweek_first_booking and 1 other fieldsHigh correlation
weekofyear_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_first_booking is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_booking is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_first_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_account_created is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_bookingHigh correlation
days_from_account_created_until_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with weekofyear_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
dayofweek_first_active is highly correlated with dayofweek_first_booking and 1 other fieldsHigh correlation
weekofyear_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_first_booking is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_booking is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_first_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_account_created is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
signup_app is highly correlated with first_device_typeHigh correlation
first_device_type is highly correlated with signup_app and 1 other fieldsHigh correlation
first_browser is highly correlated with first_device_typeHigh correlation
affiliate_channel is highly correlated with affiliate_providerHigh correlation
affiliate_provider is highly correlated with affiliate_channelHigh correlation
signup_flow is highly correlated with affiliate_channel and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_booking and 4 other fieldsHigh correlation
days_from_account_created_until_booking is highly correlated with days_from_first_active_until_booking and 4 other fieldsHigh correlation
year_first_active is highly correlated with days_from_first_active_until_booking and 9 other fieldsHigh correlation
month_first_active is highly correlated with year_first_active and 7 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
dayofweek_first_active is highly correlated with dayofweek_first_booking and 1 other fieldsHigh correlation
weekofyear_first_active is highly correlated with year_first_active and 7 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 9 other fieldsHigh correlation
month_first_booking is highly correlated with year_first_active and 7 other fieldsHigh correlation
day_first_booking is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_booking is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_booking is highly correlated with year_first_active and 7 other fieldsHigh correlation
year_first_account_created is highly correlated with days_from_first_active_until_booking and 9 other fieldsHigh correlation
month_first_account_created is highly correlated with year_first_active and 7 other fieldsHigh correlation
day_first_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
dayofweek_first_account_created is highly correlated with dayofweek_first_active and 1 other fieldsHigh correlation
weekofyear_first_account_created is highly correlated with year_first_active and 7 other fieldsHigh correlation
gender is highly correlated with ageHigh correlation
age is highly correlated with genderHigh correlation
affiliate_channel is highly correlated with signup_flow and 3 other fieldsHigh correlation
affiliate_provider is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with affiliate_channel and 1 other fieldsHigh correlation
signup_app is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_device_type is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_browser is highly correlated with signup_flow and 2 other fieldsHigh correlation
country_destination is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
days_from_first_active_until_account_created is highly skewed (γ1 = 59.21177461) Skewed
signup_flow has 452543 (80.2%) zeros Zeros
days_from_first_active_until_booking has 98606 (17.5%) zeros Zeros
days_from_first_active_until_account_created has 563497 (99.9%) zeros Zeros
days_from_account_created_until_booking has 98583 (17.5%) zeros Zeros
dayofweek_first_active has 89727 (15.9%) zeros Zeros
dayofweek_first_booking has 89727 (15.9%) zeros Zeros
dayofweek_first_account_created has 89727 (15.9%) zeros Zeros

Reproduction

Analysis started2022-05-16 20:32:04.219472
Analysis finished2022-05-16 20:35:29.974449
Duration3 minutes and 25.75 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

signup_flow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.426328094
Minimum0
Maximum25
Zeros452543
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:30.107010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile24
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.591081925
Coefficient of variation (CV)2.716484198
Kurtosis6.445504223
Mean2.426328094
Median Absolute Deviation (MAD)0
Skewness2.81998725
Sum1368961
Variance43.44236094
MonotonicityNot monotonic
2022-05-16T17:35:30.242558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0452543
80.2%
329387
 
5.2%
2527560
 
4.9%
219425
 
3.4%
1217682
 
3.1%
248491
 
1.5%
234616
 
0.8%
11591
 
0.3%
81296
 
0.2%
6949
 
0.2%
Other values (7)671
 
0.1%
ValueCountFrequency (%)
0452543
80.2%
11591
 
0.3%
219425
 
3.4%
329387
 
5.2%
41
 
< 0.1%
531
 
< 0.1%
6949
 
0.2%
81296
 
0.2%
102
 
< 0.1%
1217682
 
3.1%
ValueCountFrequency (%)
2527560
4.9%
248491
 
1.5%
234616
 
0.8%
21552
 
0.1%
2014
 
< 0.1%
1645
 
< 0.1%
1526
 
< 0.1%
1217682
3.1%
102
 
< 0.1%
81296
 
0.2%

days_from_first_active_until_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1940
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.5635108
Minimum0
Maximum2293
Zeros98606
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:30.412988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q3275
95-th percentile874
Maximum2293
Range2293
Interquartile range (IQR)274

Descriptive statistics

Standard deviation305.0256055
Coefficient of variation (CV)1.679993982
Kurtosis3.568337832
Mean181.5635108
Median Absolute Deviation (MAD)10
Skewness1.961718151
Sum102440130
Variance93040.62002
MonotonicityNot monotonic
2022-05-16T17:35:30.551527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
098606
 
17.5%
170367
 
12.5%
231157
 
5.5%
320444
 
3.6%
415669
 
2.8%
511772
 
2.1%
610244
 
1.8%
79008
 
1.6%
87345
 
1.3%
96603
 
1.2%
Other values (1930)282996
50.2%
ValueCountFrequency (%)
098606
17.5%
170367
12.5%
231157
 
5.5%
320444
 
3.6%
415669
 
2.8%
511772
 
2.1%
610244
 
1.8%
79008
 
1.6%
87345
 
1.3%
96603
 
1.2%
ValueCountFrequency (%)
22931
 
< 0.1%
22281
 
< 0.1%
20012
< 0.1%
19991
 
< 0.1%
19952
< 0.1%
19941
 
< 0.1%
19921
 
< 0.1%
19913
< 0.1%
19902
< 0.1%
19821
 
< 0.1%

days_from_first_active_until_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct142
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2856307303
Minimum0
Maximum1456
Zeros563497
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:30.692080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.51771308
Coefficient of variation (CV)43.82481206
Kurtosis4152.152908
Mean0.2856307303
Median Absolute Deviation (MAD)0
Skewness59.21177461
Sum161156
Variance156.6931407
MonotonicityNot monotonic
2022-05-16T17:35:30.806697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0563497
99.9%
569
 
< 0.1%
7861
 
< 0.1%
27450
 
< 0.1%
135
 
< 0.1%
335
 
< 0.1%
51634
 
< 0.1%
63434
 
< 0.1%
23730
 
< 0.1%
624
 
< 0.1%
Other values (132)342
 
0.1%
ValueCountFrequency (%)
0563497
99.9%
135
 
< 0.1%
25
 
< 0.1%
335
 
< 0.1%
42
 
< 0.1%
569
 
< 0.1%
624
 
< 0.1%
76
 
< 0.1%
92
 
< 0.1%
107
 
< 0.1%
ValueCountFrequency (%)
14561
 
< 0.1%
13691
 
< 0.1%
13611
 
< 0.1%
11481
 
< 0.1%
10361
 
< 0.1%
10181
 
< 0.1%
101122
< 0.1%
9981
 
< 0.1%
9951
 
< 0.1%
8821
 
< 0.1%

days_from_account_created_until_booking
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1963
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.2778801
Minimum-349
Maximum2001
Zeros98583
Zeros (%)17.5%
Negative220
Negative (%)< 0.1%
Memory size4.3 MiB
2022-05-16T17:35:30.927823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q11
median10
Q3274
95-th percentile873
Maximum2001
Range2350
Interquartile range (IQR)273

Descriptive statistics

Standard deviation304.8293877
Coefficient of variation (CV)1.681558652
Kurtosis3.569870883
Mean181.2778801
Median Absolute Deviation (MAD)10
Skewness1.961996198
Sum102278974
Variance92920.95563
MonotonicityNot monotonic
2022-05-16T17:35:31.046669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
098583
 
17.5%
170324
 
12.5%
231176
 
5.5%
320450
 
3.6%
415655
 
2.8%
511733
 
2.1%
610247
 
1.8%
79018
 
1.6%
87346
 
1.3%
96601
 
1.2%
Other values (1953)283078
50.2%
ValueCountFrequency (%)
-3491
 
< 0.1%
-3471
 
< 0.1%
-3381
 
< 0.1%
-3081
 
< 0.1%
-29817
 
< 0.1%
-2951
 
< 0.1%
-26950
< 0.1%
-2611
 
< 0.1%
-2081
 
< 0.1%
-1671
 
< 0.1%
ValueCountFrequency (%)
20012
< 0.1%
19991
 
< 0.1%
19952
< 0.1%
19941
 
< 0.1%
19921
 
< 0.1%
19913
< 0.1%
19902
< 0.1%
19821
 
< 0.1%
19801
 
< 0.1%
19791
 
< 0.1%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.984816
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:31.143854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9420049634
Coefficient of variation (CV)0.0004679642667
Kurtosis0.03781470983
Mean2012.984816
Median Absolute Deviation (MAD)1
Skewness-0.7333731123
Sum1135748176
Variance0.887373351
MonotonicityNot monotonic
2022-05-16T17:35:31.225094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013218473
38.7%
2014192347
34.1%
2012112103
19.9%
201135062
 
6.2%
20106217
 
1.1%
20099
 
< 0.1%
ValueCountFrequency (%)
20099
 
< 0.1%
20106217
 
1.1%
201135062
 
6.2%
2012112103
19.9%
2013218473
38.7%
2014192347
34.1%
ValueCountFrequency (%)
2014192347
34.1%
2013218473
38.7%
2012112103
19.9%
201135062
 
6.2%
20106217
 
1.1%
20099
 
< 0.1%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.951323175
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:31.310808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.137649356
Coefficient of variation (CV)0.527218782
Kurtosis-0.8781805789
Mean5.951323175
Median Absolute Deviation (MAD)2
Skewness0.2825451161
Sum3357802
Variance9.84484348
MonotonicityNot monotonic
2022-05-16T17:35:31.397583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
575865
13.4%
674972
13.3%
460024
10.6%
354246
9.6%
243414
7.7%
142308
7.5%
841167
7.3%
940653
7.2%
735863
6.4%
1032731
5.8%
Other values (2)62968
11.2%
ValueCountFrequency (%)
142308
7.5%
243414
7.7%
354246
9.6%
460024
10.6%
575865
13.4%
674972
13.3%
735863
6.4%
841167
7.3%
940653
7.2%
1032731
5.8%
ValueCountFrequency (%)
1230463
5.4%
1132505
5.8%
1032731
5.8%
940653
7.2%
841167
7.3%
735863
6.4%
674972
13.3%
575865
13.4%
460024
10.6%
354246
9.6%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.88474347
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:31.502276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.754571152
Coefficient of variation (CV)0.5511307857
Kurtosis-1.197873832
Mean15.88474347
Median Absolute Deviation (MAD)8
Skewness-0.008580829205
Sum8962347
Variance76.64251605
MonotonicityNot monotonic
2022-05-16T17:35:31.603933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2820374
 
3.6%
2419817
 
3.5%
319759
 
3.5%
1319729
 
3.5%
1719522
 
3.5%
1619405
 
3.4%
519340
 
3.4%
2319170
 
3.4%
1018967
 
3.4%
2118863
 
3.3%
Other values (21)369265
65.4%
ValueCountFrequency (%)
115686
2.8%
217181
3.0%
319759
3.5%
417484
3.1%
519340
3.4%
618008
3.2%
718591
3.3%
818677
3.3%
918583
3.3%
1018967
3.4%
ValueCountFrequency (%)
319767
1.7%
3018573
3.3%
2916438
2.9%
2820374
3.6%
2718279
3.2%
2618781
3.3%
2518718
3.3%
2419817
3.5%
2319170
3.4%
2218729
3.3%

dayofweek_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.772411385
Minimum0
Maximum6
Zeros89727
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:31.734231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.967624831
Coefficient of variation (CV)0.7097160405
Kurtosis-1.177793694
Mean2.772411385
Median Absolute Deviation (MAD)2
Skewness0.1633550305
Sum1564225
Variance3.871547475
MonotonicityNot monotonic
2022-05-16T17:35:31.807515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
190757
16.1%
289771
15.9%
089727
15.9%
384682
15.0%
474301
13.2%
667811
12.0%
567162
11.9%
ValueCountFrequency (%)
089727
15.9%
190757
16.1%
289771
15.9%
384682
15.0%
474301
13.2%
567162
11.9%
667811
12.0%
ValueCountFrequency (%)
667811
12.0%
567162
11.9%
474301
13.2%
384682
15.0%
289771
15.9%
190757
16.1%
089727
15.9%

weekofyear_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.10089842
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:31.910532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q335
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.58975736
Coefficient of variation (CV)0.5638693266
Kurtosis-0.8683530105
Mean24.10089842
Median Absolute Deviation (MAD)10
Skewness0.2848310216
Sum13597992
Variance184.6815052
MonotonicityNot monotonic
2022-05-16T17:35:32.035615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2618834
 
3.3%
2317779
 
3.2%
1917700
 
3.1%
2117383
 
3.1%
2516884
 
3.0%
2416851
 
3.0%
2216727
 
3.0%
2016721
 
3.0%
1816177
 
2.9%
1714942
 
2.6%
Other values (43)394213
69.9%
ValueCountFrequency (%)
18253
1.5%
27863
1.4%
310277
1.8%
410802
1.9%
59977
1.8%
610508
1.9%
710443
1.9%
811567
2.1%
912161
2.2%
1011835
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
526655
1.2%
516531
1.2%
506932
1.2%
497364
1.3%
486803
1.2%
477589
1.3%
468073
1.4%
457738
1.4%
447177
1.3%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.984816
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:32.164526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9420049634
Coefficient of variation (CV)0.0004679642667
Kurtosis0.03781470983
Mean2012.984816
Median Absolute Deviation (MAD)1
Skewness-0.7333731123
Sum1135748176
Variance0.887373351
MonotonicityNot monotonic
2022-05-16T17:35:32.247297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013218473
38.7%
2014192347
34.1%
2012112103
19.9%
201135062
 
6.2%
20106217
 
1.1%
20099
 
< 0.1%
ValueCountFrequency (%)
20099
 
< 0.1%
20106217
 
1.1%
201135062
 
6.2%
2012112103
19.9%
2013218473
38.7%
2014192347
34.1%
ValueCountFrequency (%)
2014192347
34.1%
2013218473
38.7%
2012112103
19.9%
201135062
 
6.2%
20106217
 
1.1%
20099
 
< 0.1%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.951323175
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:32.334951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.137649356
Coefficient of variation (CV)0.527218782
Kurtosis-0.8781805789
Mean5.951323175
Median Absolute Deviation (MAD)2
Skewness0.2825451161
Sum3357802
Variance9.84484348
MonotonicityNot monotonic
2022-05-16T17:35:32.424229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
575865
13.4%
674972
13.3%
460024
10.6%
354246
9.6%
243414
7.7%
142308
7.5%
841167
7.3%
940653
7.2%
735863
6.4%
1032731
5.8%
Other values (2)62968
11.2%
ValueCountFrequency (%)
142308
7.5%
243414
7.7%
354246
9.6%
460024
10.6%
575865
13.4%
674972
13.3%
735863
6.4%
841167
7.3%
940653
7.2%
1032731
5.8%
ValueCountFrequency (%)
1230463
5.4%
1132505
5.8%
1032731
5.8%
940653
7.2%
841167
7.3%
735863
6.4%
674972
13.3%
575865
13.4%
460024
10.6%
354246
9.6%

day_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.88474347
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:32.525887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.754571152
Coefficient of variation (CV)0.5511307857
Kurtosis-1.197873832
Mean15.88474347
Median Absolute Deviation (MAD)8
Skewness-0.008580829205
Sum8962347
Variance76.64251605
MonotonicityNot monotonic
2022-05-16T17:35:32.629548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2820374
 
3.6%
2419817
 
3.5%
319759
 
3.5%
1319729
 
3.5%
1719522
 
3.5%
1619405
 
3.4%
519340
 
3.4%
2319170
 
3.4%
1018967
 
3.4%
2118863
 
3.3%
Other values (21)369265
65.4%
ValueCountFrequency (%)
115686
2.8%
217181
3.0%
319759
3.5%
417484
3.1%
519340
3.4%
618008
3.2%
718591
3.3%
818677
3.3%
918583
3.3%
1018967
3.4%
ValueCountFrequency (%)
319767
1.7%
3018573
3.3%
2916438
2.9%
2820374
3.6%
2718279
3.2%
2618781
3.3%
2518718
3.3%
2419817
3.5%
2319170
3.4%
2218729
3.3%

dayofweek_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.772411385
Minimum0
Maximum6
Zeros89727
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:32.728449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.967624831
Coefficient of variation (CV)0.7097160405
Kurtosis-1.177793694
Mean2.772411385
Median Absolute Deviation (MAD)2
Skewness0.1633550305
Sum1564225
Variance3.871547475
MonotonicityNot monotonic
2022-05-16T17:35:32.802203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
190757
16.1%
289771
15.9%
089727
15.9%
384682
15.0%
474301
13.2%
667811
12.0%
567162
11.9%
ValueCountFrequency (%)
089727
15.9%
190757
16.1%
289771
15.9%
384682
15.0%
474301
13.2%
567162
11.9%
667811
12.0%
ValueCountFrequency (%)
667811
12.0%
567162
11.9%
474301
13.2%
384682
15.0%
289771
15.9%
190757
16.1%
089727
15.9%

weekofyear_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.10089842
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:32.905858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q335
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.58975736
Coefficient of variation (CV)0.5638693266
Kurtosis-0.8683530105
Mean24.10089842
Median Absolute Deviation (MAD)10
Skewness0.2848310216
Sum13597992
Variance184.6815052
MonotonicityNot monotonic
2022-05-16T17:35:33.028448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2618834
 
3.3%
2317779
 
3.2%
1917700
 
3.1%
2117383
 
3.1%
2516884
 
3.0%
2416851
 
3.0%
2216727
 
3.0%
2016721
 
3.0%
1816177
 
2.9%
1714942
 
2.6%
Other values (43)394213
69.9%
ValueCountFrequency (%)
18253
1.5%
27863
1.4%
310277
1.8%
410802
1.9%
59977
1.8%
610508
1.9%
710443
1.9%
811567
2.1%
912161
2.2%
1011835
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
526655
1.2%
516531
1.2%
506932
1.2%
497364
1.3%
486803
1.2%
477589
1.3%
468073
1.4%
457738
1.4%
447177
1.3%

year_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.984816
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:33.132103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9420049634
Coefficient of variation (CV)0.0004679642667
Kurtosis0.03781470983
Mean2012.984816
Median Absolute Deviation (MAD)1
Skewness-0.7333731123
Sum1135748176
Variance0.887373351
MonotonicityNot monotonic
2022-05-16T17:35:33.215362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013218473
38.7%
2014192347
34.1%
2012112103
19.9%
201135062
 
6.2%
20106217
 
1.1%
20099
 
< 0.1%
ValueCountFrequency (%)
20099
 
< 0.1%
20106217
 
1.1%
201135062
 
6.2%
2012112103
19.9%
2013218473
38.7%
2014192347
34.1%
ValueCountFrequency (%)
2014192347
34.1%
2013218473
38.7%
2012112103
19.9%
201135062
 
6.2%
20106217
 
1.1%
20099
 
< 0.1%

month_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.951323175
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:33.306061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.137649356
Coefficient of variation (CV)0.527218782
Kurtosis-0.8781805789
Mean5.951323175
Median Absolute Deviation (MAD)2
Skewness0.2825451161
Sum3357802
Variance9.84484348
MonotonicityNot monotonic
2022-05-16T17:35:33.393765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
575865
13.4%
674972
13.3%
460024
10.6%
354246
9.6%
243414
7.7%
142308
7.5%
841167
7.3%
940653
7.2%
735863
6.4%
1032731
5.8%
Other values (2)62968
11.2%
ValueCountFrequency (%)
142308
7.5%
243414
7.7%
354246
9.6%
460024
10.6%
575865
13.4%
674972
13.3%
735863
6.4%
841167
7.3%
940653
7.2%
1032731
5.8%
ValueCountFrequency (%)
1230463
5.4%
1132505
5.8%
1032731
5.8%
940653
7.2%
841167
7.3%
735863
6.4%
674972
13.3%
575865
13.4%
460024
10.6%
354246
9.6%

day_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.88474347
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:33.497953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.754571152
Coefficient of variation (CV)0.5511307857
Kurtosis-1.197873832
Mean15.88474347
Median Absolute Deviation (MAD)8
Skewness-0.008580829205
Sum8962347
Variance76.64251605
MonotonicityNot monotonic
2022-05-16T17:35:33.603377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2820374
 
3.6%
2419817
 
3.5%
319759
 
3.5%
1319729
 
3.5%
1719522
 
3.5%
1619405
 
3.4%
519340
 
3.4%
2319170
 
3.4%
1018967
 
3.4%
2118863
 
3.3%
Other values (21)369265
65.4%
ValueCountFrequency (%)
115686
2.8%
217181
3.0%
319759
3.5%
417484
3.1%
519340
3.4%
618008
3.2%
718591
3.3%
818677
3.3%
918583
3.3%
1018967
3.4%
ValueCountFrequency (%)
319767
1.7%
3018573
3.3%
2916438
2.9%
2820374
3.6%
2718279
3.2%
2618781
3.3%
2518718
3.3%
2419817
3.5%
2319170
3.4%
2218729
3.3%

dayofweek_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.772411385
Minimum0
Maximum6
Zeros89727
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:33.703045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.967624831
Coefficient of variation (CV)0.7097160405
Kurtosis-1.177793694
Mean2.772411385
Median Absolute Deviation (MAD)2
Skewness0.1633550305
Sum1564225
Variance3.871547475
MonotonicityNot monotonic
2022-05-16T17:35:33.778452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
190757
16.1%
289771
15.9%
089727
15.9%
384682
15.0%
474301
13.2%
667811
12.0%
567162
11.9%
ValueCountFrequency (%)
089727
15.9%
190757
16.1%
289771
15.9%
384682
15.0%
474301
13.2%
567162
11.9%
667811
12.0%
ValueCountFrequency (%)
667811
12.0%
567162
11.9%
474301
13.2%
384682
15.0%
289771
15.9%
190757
16.1%
089727
15.9%

weekofyear_first_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.10089842
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:33.885076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q335
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.58975736
Coefficient of variation (CV)0.5638693266
Kurtosis-0.8683530105
Mean24.10089842
Median Absolute Deviation (MAD)10
Skewness0.2848310216
Sum13597992
Variance184.6815052
MonotonicityNot monotonic
2022-05-16T17:35:34.012655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2618834
 
3.3%
2317779
 
3.2%
1917700
 
3.1%
2117383
 
3.1%
2516884
 
3.0%
2416851
 
3.0%
2216727
 
3.0%
2016721
 
3.0%
1816177
 
2.9%
1714942
 
2.6%
Other values (43)394213
69.9%
ValueCountFrequency (%)
18253
1.5%
27863
1.4%
310277
1.8%
410802
1.9%
59977
1.8%
610508
1.9%
710443
1.9%
811567
2.1%
912161
2.2%
1011835
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
526655
1.2%
516531
1.2%
506932
1.2%
497364
1.3%
486803
1.2%
477589
1.3%
468073
1.4%
457738
1.4%
447177
1.3%

gender
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
-unknown-
203055 
FEMALE
190998 
MALE
168896 
OTHER
 
1262

Length

Max length9
Median length6
Mean length6.478741109
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th row-unknown-

Common Values

ValueCountFrequency (%)
-unknown-203055
36.0%
FEMALE190998
33.9%
MALE168896
29.9%
OTHER1262
 
0.2%

Length

2022-05-16T17:35:34.128774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-16T17:35:34.197032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown203055
36.0%
female190998
33.9%
male168896
29.9%
other1262
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.85744518
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-16T17:35:34.284741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q131
median40
Q349
95-th percentile60
Maximum115
Range99
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.9449027
Coefficient of variation (CV)0.3168309386
Kurtosis4.407622748
Mean40.85744518
Median Absolute Deviation (MAD)9
Skewness1.239106451
Sum23052220
Variance167.570506
MonotonicityNot monotonic
2022-05-16T17:35:34.404340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49169734
30.1%
3020308
 
3.6%
3119571
 
3.5%
3219403
 
3.4%
2919184
 
3.4%
2817616
 
3.1%
3317123
 
3.0%
2716642
 
2.9%
3416559
 
2.9%
3514984
 
2.7%
Other values (89)233087
41.3%
ValueCountFrequency (%)
1626
 
< 0.1%
1781
 
< 0.1%
182572
 
0.5%
194754
 
0.8%
201273
 
0.2%
213939
 
0.7%
226267
1.1%
238152
1.4%
2410839
1.9%
2514004
2.5%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110357
 
0.1%
10933
 
< 0.1%
10815
 
< 0.1%
10723
 
< 0.1%
10628
 
< 0.1%
1054417
0.8%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
basic
408915 
facebook
154394 
google
 
902

Length

Max length8
Median length5
Mean length5.82253625
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowbasic
4th rowfacebook
5th rowbasic

Common Values

ValueCountFrequency (%)
basic408915
72.5%
facebook154394
 
27.4%
google902
 
0.2%

Length

2022-05-16T17:35:34.520951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-16T17:35:34.593708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
basic408915
72.5%
facebook154394
 
27.4%
google902
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
en
548140 
fr
 
3226
zh
 
2491
de
 
2409
es
 
2295
Other values (20)
 
5650

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en548140
97.2%
fr3226
 
0.6%
zh2491
 
0.4%
de2409
 
0.4%
es2295
 
0.4%
ko1384
 
0.2%
it1128
 
0.2%
ru737
 
0.1%
pt495
 
0.1%
ja432
 
0.1%
Other values (15)1474
 
0.3%

Length

2022-05-16T17:35:34.659490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en548140
97.2%
fr3226
 
0.6%
zh2491
 
0.4%
de2409
 
0.4%
es2295
 
0.4%
ko1384
 
0.2%
it1128
 
0.2%
ru737
 
0.1%
pt495
 
0.1%
ja432
 
0.1%
Other values (15)1474
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
direct
372131 
sem-brand
73791 
sem-non-brand
51928 
seo
 
23817
other
 
17155
Other values (3)
 
25389

Length

Max length13
Median length6
Mean length6.832964972
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct372131
66.0%
sem-brand73791
 
13.1%
sem-non-brand51928
 
9.2%
seo23817
 
4.2%
other17155
 
3.0%
api15797
 
2.8%
content6716
 
1.2%
remarketing2876
 
0.5%

Length

2022-05-16T17:35:34.749191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-16T17:35:34.821010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
direct372131
66.0%
sem-brand73791
 
13.1%
sem-non-brand51928
 
9.2%
seo23817
 
4.2%
other17155
 
3.0%
api15797
 
2.8%
content6716
 
1.2%
remarketing2876
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
direct
371182 
google
141935 
other
 
24992
craigslist
 
6613
bing
 
6573
Other values (13)
 
12916

Length

Max length19
Median length6
Mean length6.030770403
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct371182
65.8%
google141935
 
25.2%
other24992
 
4.4%
craigslist6613
 
1.2%
bing6573
 
1.2%
facebook6250
 
1.1%
vast1753
 
0.3%
facebook-open-graph1286
 
0.2%
padmapper1212
 
0.2%
yahoo918
 
0.2%
Other values (8)1497
 
0.3%

Length

2022-05-16T17:35:34.929656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct371182
65.8%
google141935
 
25.2%
other24992
 
4.4%
craigslist6613
 
1.2%
bing6573
 
1.2%
facebook6250
 
1.1%
vast1753
 
0.3%
facebook-open-graph1286
 
0.2%
padmapper1212
 
0.2%
yahoo918
 
0.2%
Other values (8)1497
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_affiliate_tracked
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
untracked
304407 
linked
123296 
omg
118337 
tracked-other
 
12851
product
 
4961
Other values (2)
 
359

Length

Max length13
Median length9
Mean length7.159504157
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked304407
54.0%
linked123296
21.9%
omg118337
 
21.0%
tracked-other12851
 
2.3%
product4961
 
0.9%
marketing231
 
< 0.1%
local ops128
 
< 0.1%

Length

2022-05-16T17:35:35.030312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-16T17:35:35.099568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
untracked304407
53.9%
linked123296
21.8%
omg118337
 
21.0%
tracked-other12851
 
2.3%
product4961
 
0.9%
marketing231
 
< 0.1%
local128
 
< 0.1%
ops128
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_app
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Web
507444 
iOS
 
36163
Moweb
 
11758
Android
 
8846

Length

Max length7
Median length3
Mean length3.104393569
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web507444
89.9%
iOS36163
 
6.4%
Moweb11758
 
2.1%
Android8846
 
1.6%

Length

2022-05-16T17:35:35.200763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-16T17:35:35.271525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
web507444
89.9%
ios36163
 
6.4%
moweb11758
 
2.1%
android8846
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_device_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Mac Desktop
269616 
Windows Desktop
194464 
iPhone
41217 
iPad
39371 
Other/Unknown
 
8131
Other values (4)
 
11412

Length

Max length18
Median length11
Mean length11.6132298
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowMac Desktop
3rd rowWindows Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop269616
47.8%
Windows Desktop194464
34.5%
iPhone41217
 
7.3%
iPad39371
 
7.0%
Other/Unknown8131
 
1.4%
Android Phone4551
 
0.8%
Desktop (Other)3458
 
0.6%
Android Tablet3300
 
0.6%
SmartPhone (Other)103
 
< 0.1%

Length

2022-05-16T17:35:35.351259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-16T17:35:35.429999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
desktop467538
45.0%
mac269616
25.9%
windows194464
18.7%
iphone41217
 
4.0%
ipad39371
 
3.8%
other/unknown8131
 
0.8%
android7851
 
0.8%
phone4551
 
0.4%
other3561
 
0.3%
tablet3300
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_browser
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Chrome
183051 
Safari
132750 
Firefox
98993 
IE
53527 
Mobile Safari
48171 
Other values (47)
47719 

Length

Max length20
Median length6
Mean length6.683847001
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowChrome
3rd rowIE
4th rowFirefox
5th rowChrome

Common Values

ValueCountFrequency (%)
Chrome183051
32.4%
Safari132750
23.5%
Firefox98993
17.5%
IE53527
 
9.5%
Mobile Safari48171
 
8.5%
-unknown-41219
 
7.3%
Chrome Mobile2767
 
0.5%
Android Browser1581
 
0.3%
Opera438
 
0.1%
AOL Explorer395
 
0.1%
Other values (42)1319
 
0.2%

Length

2022-05-16T17:35:35.560613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome185818
30.1%
safari180921
29.3%
firefox99045
16.0%
ie53563
 
8.7%
mobile51028
 
8.3%
unknown41219
 
6.7%
browser1777
 
0.3%
android1581
 
0.3%
opera444
 
0.1%
explorer440
 
0.1%
Other values (48)1746
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country_destination
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
NDF
119810 
US
60800 
other
49675 
IT
41640 
ES
39654 
Other values (7)
252632 

Length

Max length5
Median length2
Mean length2.476479544
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowUS
4th rowother
5th rowUS

Common Values

ValueCountFrequency (%)
NDF119810
21.2%
US60800
10.8%
other49675
8.8%
IT41640
 
7.4%
ES39654
 
7.0%
GB38845
 
6.9%
CA37395
 
6.6%
NL37300
 
6.6%
AU36820
 
6.5%
DE36155
 
6.4%
Other values (2)66117
11.7%

Length

2022-05-16T17:35:35.666253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf119810
21.2%
us60800
10.8%
other49675
8.8%
it41640
 
7.4%
es39654
 
7.0%
gb38845
 
6.9%
ca37395
 
6.6%
nl37300
 
6.6%
au36820
 
6.5%
de36155
 
6.4%
Other values (2)66117
11.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-16T17:35:15.420884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:09.857405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:15.461660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:21.035115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:26.303606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:32.152037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:38.692603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:44.881574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:50.501830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:56.581844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:03.495696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:10.552027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:17.693792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:23.953256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:30.583017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:42.496401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:50.650154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:56.837905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:03.014163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:09.245301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:15.798625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:10.131204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:15.726749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:21.276671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:26.571843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:32.461431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:39.016822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:45.154912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:50.771809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:56.879964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:03.845553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:10.831951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:18.004753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:24.312837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:30.939142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:43.102376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:50.962105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-16T17:34:54.084591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:00.163323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:06.497383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:12.561780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:20.369205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:13.201317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:18.811686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:24.041491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:29.751467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:36.049038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:42.452953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:48.191856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:53.969068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:00.653068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:07.661871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:14.421410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:21.375679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:27.930943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:36.856676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:47.914237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:54.392738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:00.474280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:06.793911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:12.860730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:20.847604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:13.556605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:19.081736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:24.286680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:30.021595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:36.383516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:42.726695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:48.481709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:54.237063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:01.096578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:08.016845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:14.779564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:21.666421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:28.318202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:37.790917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:48.227266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:54.692244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:00.817229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:07.099459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:13.162443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:21.251778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:13.806337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:19.346597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:24.551686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:30.296657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:36.681531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:42.991769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:48.756765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:54.511962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:01.423487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:08.362082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:15.358457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:21.954231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:28.725841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:38.473913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:48.604284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:54.980882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:01.110375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:07.388503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:13.460933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:21.634729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:14.056366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:19.623434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:24.801760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:30.610358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:36.961868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:43.266480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:49.031775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:54.786987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:01.738442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:08.709728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:15.735498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:22.294855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:29.023781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:39.252228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:49.035625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:55.270968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:01.444005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:07.690495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:13.759939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:22.017562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:14.301334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:19.891759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:25.051670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:30.902352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:37.261959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:43.554413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:49.311894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:55.057024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:02.057029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:09.007397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:16.077655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:22.617424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:29.298715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:39.866178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:49.335623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:55.575953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:01.738224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:07.978583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:14.059972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:22.399351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:14.586660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:20.161616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:25.291680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:31.190974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:37.581443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:43.831935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:49.596777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:55.331961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:02.473060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:09.321810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:16.584715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:22.905797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:29.607031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:40.496075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:49.657753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:55.875459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:02.041243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:08.276588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:14.362129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:22.808613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:14.836740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:20.441773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:25.561375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:31.501841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:37.906490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:44.104841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:49.871965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:55.616979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:02.786062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:09.697042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:16.947026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:23.225203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:29.882128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:41.142916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:49.962728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:56.171024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:02.344906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:08.570689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:14.663405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:23.436045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:15.181386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:20.796489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:25.921699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:31.866437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:38.354515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:44.561620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:50.222020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:33:56.290849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:03.194699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:10.261741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:17.352172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:23.612688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:30.236839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:42.105702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:50.365890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:34:56.550807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:02.731614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:08.952776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-16T17:35:15.046138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-16T17:35:35.777329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-16T17:35:36.046498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-16T17:35:36.395359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-16T17:35:36.760141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-16T17:35:37.040262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-16T17:35:24.583463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-16T17:35:27.014866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

signup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekofyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_account_createdmonth_first_account_createdday_first_account_createddayofweek_first_account_createdweekofyear_first_account_createdgenderagesignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
0022934661827200931931220093193122009319312-unknown-49facebookendirectdirectuntrackedWebMac DesktopChromeNDF
1022287321496200952352120095235212009523521MALE38facebookenseogoogleuntrackedWebMac DesktopChromeNDF
23419476-57200969124200969124200969124FEMALE56basicendirectdirectuntrackedWebWindows DesktopIEUS
301043765278200910315442009103154420091031544FEMALE42facebookendirectdirectuntrackedWebMac DesktopFirefoxother
4072280-208200912815020091281502009128150-unknown-41basicendirectdirectuntrackedWebMac DesktopChromeUS
50101201011453201011453201011453-unknown-49basicenotherotheromgWebMac DesktopChromeUS
60303201012553201012553201012553FEMALE46basicenothercraigslistuntrackedWebMac DesktopSafariUS
7010010201013653201013653201013653FEMALE47basicendirectdirectomgWebMac DesktopSafariUS
802060206201014012010140120101401FEMALE50basicenothercraigslistuntrackedWebMac DesktopSafariUS
90000201014012010140120101401-unknown-46basicenothercraigslistomgWebMac DesktopFirefoxUS

Last rows

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564206026026201252322120125232212012523221FEMALE34facebookendirectdirectuntrackedWebMac DesktopChromeother
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5642090000201452012120145201212014520121FEMALE31basicendirectdirectuntrackedWebMac DesktopSafariother
564210015810148201381203320138120332013812033FEMALE39facebookendirectdirectuntrackedWebAndroid PhoneChrome Mobileother

Duplicate rows

Most frequently occurring

signup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekofyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_account_createdmonth_first_account_createdday_first_account_createddayofweek_first_account_createdweekofyear_first_account_createdgenderagesignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination# duplicates
15956030030201312274522013122745220131227452MALE30basicensem-brandgoogleomgWebMac DesktopFirefoxPT185
108830404201421227201421227201421227-unknown-49basicenseogoogleuntrackedWebWindows DesktopChromePT182
20370000201391713820139171382013917138FEMALE28basicendirectdirectuntrackedWebMac DesktopChromePT176
313833000201182163320118216332011821633MALE67basicendirectdirectuntrackedWebWindows DesktopFirefoxPT176
42600000201461562420146156242014615624-unknown-33basicensem-brandgoogleuntrackedWebWindows DesktopChromePT174
106470404201351412020135141202013514120-unknown-57basicensem-brandgoogleomgWebWindows DesktopIEPT174
109570404201444414201444414201444414-unknown-49basicensem-non-brandgoogleomgWebWindows DesktopIEPT174
3533125202201387232201387232201387232MALE25basicendirectdirectuntrackediOSiPhone-unknown-PT174
13424010010201392043820139204382013920438MALE34basicensem-brandgoogleomgWebMac DesktopChromePT173
21480000201310334020131033402013103340FEMALE34facebookendirectdirecttracked-otherWebMac DesktopSafariPT172